学术报告(6月30日)To Tell if Image Retargeting Is Well-done: Using Dense Correspondence and Multi-level Features

发布者:潘春德发布时间:2018-06-25浏览次数:95

    受中国矿业大学信息与控制工程学院邀请,新加坡南洋理工大学Weisi Lin教授在我校举行学术报告。欢迎广大师生踊跃参加!

  

报告题目:To Tell if Image Retargeting Is Well-done: Using Dense Correspondence and Multi-level Features

间:2018年6月30日,上午10:00

点:南湖校区信控学院A311

主办单位:信息与控制工程学院

Abstract: There are numerous situations where images need to be auto-adapted into different sizes and compositions. For instance, the increasing diversity of visual display devices (especially mobile ones) has demanded for effective image adaptation (retargeting) for any given aspect ratio; automatic (or semi-automatic) image editing is required for Photoshop (and other similar tools), native advertising, webpage content creation, post-processing of TV/movies, VR/AR scenarios, etc; there will be also future possibilities for reducing the amount of visual data  transmitted over the wireless network (with low bit-rates) while ensuring the required accuracy for a specific AI task. To date, there is not a single image retargeting method that works consistently well for all visual content, and this call for effective image retargeting quality assessment (IRQA) to be developed for on-line benchmarking and then selecting the best retargeting scheme dynamically at servers; furthermore, IRQA facilitates the advancement of image retargeting techniques themselves. In this talk, a new IRQA methodology is presented, being able to predict the perceived retargeting quality by proper evaluation of salient content loss and visual distortion. Its key contribution lies in dense correspondence (DC) estimation and multi-level feature (MLF) fusion: the former makes IRQA truly full-reference assessment for the first time, while the latter enables better agreement with human appreciation and adjustment.   

  

Bio: Weisi Lin is an active researcher in image processing, perception-based signal modelling and assessment, video compression, and multimedia communication systems. In the said areas, he has published 180+ international journal papers and 230+ international conference papers, 7 patents, 9 book chapters, 2 authored books and 3 edited books, as well as excellent track record in leading and delivering more than 10 major funded projects (with over S$7m research funding). He earned his Ph.D from King’s College, University of London. He had been the Lab Head, Visual Processing, in Institute for Infocomm Research (I2R). He is a Professor in School of Computer Science and Engineering, Nanyang Technological University, where he served as the Associate Chair (Graduate Studies) in 2013-2014. 

He is a Fellow of IEEE and IET, and an Honorary Fellow of Singapore Institute of Engineering Technologists. He has been elected as a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13), and given keynote/invited/tutorial/panel talks to 20+ international conferences during the past 10 years.  He has been an Associate Editor for IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology, IEEE Trans. on Multimedia, IEEE Signal Processing Letters, Quality and User Experience, and Journal of Visual Communication and Image Representation. He was also the Guest Editor for 7 special issues in international journals, and chaired the IEEE MMTC QoE Interest Group (2012-2014); he has been a Technical Program Chair for IEEE Int’l Conf. Multimedia and Expo (ICME 2013), International Workshop on Quality of Multimedia Experience (QoMEX 2014), International Packet Video Workshop (PV 2015), Pacific-Rim Conf. on Multimedia (PCM 2012) and IEEE Visual Communications and Image Processing (VCIP 2017). He believes that good theory is practical, and has delivered 10 major systems and modules for industrial deployment with the technology developed.